A direct demand model for bus transit ridership in Bengaluru, India. (September 2022) - Deepa, L.; Rawoof Pinjari, Abdul; Krishna Nirmale, Sangram; Srinivasan, Karthik K.; Rambha, Tarun
Highlights: A disaggregate direct demand model of bus transit ridership in Bengaluru, India. Log-sum variable as an empirical strategy to relate spatially aggregated demand data to disaggregate stop-level catchment area characteristics. Route-level service frequency is endogenous to and has a non-linear impact on stop-level ridership. Inter-route relationships (competition and complementarity among routes) within the bus network and with the Metro network significantly influence bus ridership. The proposed framework and empirical strategies are applicable to analyze transit ridership in other cities. Abstract: This study formulates a disaggregate direct demand model of bus transit ridership while addressing the following substantive and methodological issues: (a) endogeneity and non-linearity of the influence of service frequency on ridership, (b) inter-route relationships such as competition and complementarity among routes within the bus transit network and with other transit networks (such as the metro/rail network), (c) relating spatially aggregated demand to disaggregate, stop-level catchment characteristics – although demand data are available only at an aggregation of stop-clusters, and (d) overlapping of catchment areas among closely spaced stops. The proposed model is applied to analyze bus transit ridership (boardings) during weekdays for morning peak period in Bengaluru, India. This study is among the first to develop a comprehensive direct demand model for forecasting bus transit ridership in an Indian city. Yet, the proposed conceptual and methodological framework and the findings from the study are general enough to be of use for transit planning in other cities of India and other countries. Transit agencies with spatially aggregate, fare-stage cluster-level ridership data can employ the proposed approach to examine the influence of disaggregate stop-level catchment characteristics on ridership. Additionally, transit agencies may utilise the proposed model to quantify bus ridership impacts of service network modifications, route alignments, and network connectivity/accessibility, while considering interactions with other transit networks. The empirical results suggest that while increasing service frequency increases ridership along low-frequency routes, the returns from increasing service frequency diminish as current frequency levels increase. Further, it is shown that route-level passenger kilometres, a variable commonly available with transit agencies, serves effectively as an instrument for addressing endogeneity between route-level service frequency and stop-route-level ridership.